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Game-Tree Search over High-Level Game States in RTS Games

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38 diapositives · 580 Views · Nov 02, 2014

Game-Tree Search over High-Level Game States in RTS Games. Alberto Uriarte and Santiago Ontañón. Drexel University Philadelphia. October 6, 2014. Outline. Motivation High-level Abstraction in RTS Games Mapping Low-Level and High-Level States High-level Game-Tree Search

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Game-Tree Search over High-Level Game States in RTS Games
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  1. Game-Tree Search over High-Level Game States in RTS Games Alberto Uriarte and Santiago Ontañón Drexel University Philadelphia October 6, 2014

  2. Outline • Motivation • High-level Abstraction in RTS Games • Mapping Low-Level and High-Level States • High-level Game-Tree Search • Evaluation • Conclusions

  3. Motivation • RTS properties • Simultaneous moves • “Real-time” • Partially observable • Non deterministic

  4. Game complexity

  5. Game complexity StarCraft map: 128x128 Maximum number of units: 400 Considering only unit positions: (128x128)400=16384400≈101685

  6. Motivation

  7. Motivation Units: 50 – 200 Actions per unit: 30 Branching factor: 3050 - 30200 Length of a game: 25 minutes 25 min x 60 sec x 24 iteration per sec = = 36000

  8. High-level Abstraction in RTS games • Levels of decisions • Strategy. The whole army and buildings. • Tactics. Group of units. • Reactive Control. One unit. • We focused on tactical decisions!!

  9. High-level Abstraction in RTS games Two different abstractions: 1. Map abstraction

  10. High-level Abstraction in RTS games Two different abstractions: 1. Map abstraction Perkins’ algorithm to decompose a map into regions and chokepoints.

  11. High-level Abstraction in RTS games Two different abstractions: 1. Map abstraction New regions around chokepoint influence

  12. High-level Abstraction in RTS games Two different abstractions: 1. Map abstraction All distances are pre-computed

  13. High-level Abstraction in RTS games Two different abstractions: 2. Unit group abstraction • Hit Points (shield) • Position • Order: • move, attack, stop, patrol • repair, build, siege • Size • Damage (points and type)

  14. High-level Abstraction in RTS games Two different abstractions: 2. Unit group abstraction • Player. Which player controls this group • Type.Type of units in this group • Size. Number of units forming this group • Region. Which region is this group in • Order. Which order is currently performing • N/A, Move, Attack, Idle • Target. The ID of the target region • End.In which game frame is the order estimated to finish

  15. High-level Abstraction in RTS games We only consider military units and main bases

  16. High-level Abstraction in RTS games We only consider military units and main bases

  17. High-level Abstraction in RTS games Abstract state

  18. High-level Abstraction in RTS games Abstract state. Possible Actions

  19. High-level Abstraction in RTS games Abstract state. Possible Actions Move to 1. Move to 3. Idle

  20. High-level Abstraction in RTS games Abstract state. Possible Actions Move to 2, 4, 5 or 6. Idle

  21. High-level Abstraction in RTS games Abstract state. Possible Actions Branching factor: (1)×(2+1)×(4+1) = 15

  22. High-level Abstraction in RTS games SparCraft(Churchill, Saffidine, and Buro 2012)

  23. Mapping Low-Level and High-Level States Squad groups don’t have to be the same as abstract groups!!

  24. High-level Game-Tree Search • State forwarding (simulator) • We estimate in which game frame the group finish their order. • Moving: velocity + distance to region • Attack: DPS between groups • Next player to move: the one with the group with the smallest “end” frame.

  25. High-level Game-Tree Search • State forwarding (simulator) • We estimate in which game frame the group finish their order. • Moving: velocity + distance to region • Attack: DPS between groups • State evaluation

  26. High-level Game-Tree Search Alpha-Beta MCTS

  27. High-level Game-Tree Search Alpha-Beta MCTS ABCD(Churchill, Saffidine, and Buro 2012) UCTCD MCTSCD

  28. High-level Game-Tree Search MCTSCD

  29. High-level Game-Tree Search

  30. High-level Game-Tree Search

  31. Evaluation settings Games limited to 20 minutes (28,800 frames) Search called every 400 frames (pause game) No fog of war (future work)

  32. Evaluation settings • Games limited to 20 minutes (28,800 frames) • Search called every 400 frames (pause game) • No fog of war (future work) • ABCD parameters • Maximum depth: 3 • Children limited to 10,000 nodes • Time limited to 30 seconds • Simultaneous move: Alt policy

  33. Evaluation settings • Games limited to 20 minutes (28,800 frames) • Search called every 400 frames (pause game) • No fog of war (future work) • MCTSCD parameters • Tree policy: e-greedy (e=0.2) • Default policy: random move selection • Simultaneous move: Alt policy • Tree policy depth: limited to 10 • 2,000 playouts limited to 7,200 game frames (5 minutes)

  34. Results

  35. Results

  36. Results

  37. Conclusions and Future Work Future work Conclusions • New MCTS algorithm for simultaneous actions (MCTSCD) • High level abstraction in the right direction to be able to apply game-tree search algorithms • Improve the game tree search algorithm • different bandit strategies • deal with partial observability • More abstractions and their tradeoffs • Improve the game simulator by learning during the course of a game

  38. Game-Tree Search over High-Level Game States in RTS Games Alberto Uriarte albertouri@cs.drexel.edu Santiago Ontañónsanti@cs.drexel.edu